Genetic algorithm with local search for community mining in complex networks

Detecting communities from complex networks has triggered considerable attention in several application domains. Targeting this problem, a local search based genetic algorithm (GALS) which employs a graph-based representation (LAR) has been proposed in this work. The core of the GALS is a local search based mutation technique. Aiming to overcome the drawbacks of the existing mutation methods, a concept called marginal gene has been proposed, and then an effective and efficient mutation method, combined with a local search strategy which is based on the concept of marginal gene, has also been proposed by analyzing the modularity function. Moreover, in this paper the percolation theory on ER random graphs is employed to further clarify the effectiveness of LAR presentation; A Markov random walk based method is adopted to produce an accurate and diverse initial population; the solution space of GALS will be significantly reduced by using a graph based mechanism. The proposed GALS has been tested on both computer-generated and real-world networks, and compared with some competitive community mining algorithms. Experimental result has shown that GALS is hig y effective and efficient for discovering community structure.This work was supported by National Natural Science Foundation of China under Grant Nos. 60873149, 60973088, National High-Tech Research and Development Plan of China under Grant No. 2006AA10Z245, Open Project Program of the National Laboratory of Pattern Recognition, and BRIDGING THE GAP Erasmus Mundus project of EU. We would like to thank Mark Newman for providing us with the source code of algorithms FN and GN, and some real-world network data

Genetic algorithm with a local search strategy for discovering communities in complex networks

In order to further improve the performance of current genetic algorithms aiming at discovering communities, a local search based genetic algorithm GALS is here proposed. The core of GALS is a local search based mutation technique. In order to overcome the drawbacks of traditional mutation methods, the paper develops the concept of marginal gene and then the local monotonicity of modularity function Q is deduced from each nodes local view. Based on these two elements, a new mutation method combined with a local search strategy is presented. GALS has been evaluated on both synthetic benchmarks and several real networks, and compared with some presently competing algorithms. Experimental results show that GALS is highly effective and efficient for discovering community.Thanks are due to the referees for helpful comments. This work was supported by National Natural Science Foundation of China (60873149, 60973088, 61133011, 61202308), Scholarship Award for Excellent Doctoral Student granted by Ministry of Education (450060454018), Program for New Century Excellent Talents in University (NCET-11-0204), and Jilin University Innovation Project (450060481084)

A review of quantum-inspired metaheuristic algorithms for automatic clustering

In real-world scenarios, identifying the optimal number of clusters in a dataset is a difficult task due to insufficient knowledge. Therefore, the indispensability of sophisticated automatic clus tering algorithms for this purpose has been contemplated by some researchers. Several automatic clustering algorithms assisted by quantum-inspired metaheuristics have been developed in recent years. However, the literature lacks definitive documentation of the state-of-the-art quantum-inspired metaheuristic algorithms for automatically clustering datasets. This article presents a brief overview of the automatic clustering process to establish the importance of making the clustering process automatic. The fundamental concepts of the quantum computing paradigm are also presented to highlight the utility of quantum-inspired algorithms. This article thoroughly analyses some algo rithms employed to address the automatic clustering of various datasets. The reviewed algorithms were classified according to their main sources of inspiration. In addition, some representative works of each classification were chosen from the existing works. Thirty-six such prominent algorithms were further critically analysed based on their aims, used mechanisms, data specifications, merits and demerits. Comparative results based on the performance and optimal computational time are also presented to critically analyse the reviewed algorithms. As such, this article promises to provide a detailed analysis of the state-of-the-art quantum-inspired metaheuristic algorithms, while highlighting their merits and demerits.Web of Science119art. no. 201

Self-Organized Specialization and Controlled Emergence in Organic Computing Systems

In this chapter we studied a first approach to generate suitable rule sets for solving classification problems on systems of autonomous, memory constrained components. It was shown that a multi agent system that uses interacting Pittsburgh-style classifier systems can evolve appropiate rule sets. The system evolves specialists for parts of the classification problem and cooperation between them. In this way the components overcome their restricted memory size and are able to solve the entire problem. It was shown that the communication topology between the components strongly influences the average number of components that a request has to pass until it is classified. It was also shown that the introduction of communication costs into the fitness function leads to a more even distribution of knowledge between the components and reduces the communication overhead without influencing the classification performance very much. If the system is used to generate rule sets to solve classification tasks on real hardware systems, communication cost in the training phase can thus lead to a better knowledge distribution and small communication cost. That is, in this way the system will be more robust against the loss of single components and longer reliable in case of limited energy resources

Segmentación de usuarios en la oficina de farmacia mediante algoritmos bioinspirados

Programa de Doctorado en Estadística e Investigación OperativaLa situación de las oficinas de farmacia, desde el punto de vista del negocio, está pasando por uno de sus momentos más complejos. El entorno económico y las reformas llevadas a cabo están incidiendo en su cuenta de resultados y, de manera directa, en la dispensación de medicamentos financiados por el Sistema Nacional de Salud. Prueba de esta situación es la tendencia decreciente del gasto sanitario público en el período 2008-2012 (último periodo disponible como estadística oficial cuya publicación se denomina "Estadística de Gasto Sanitario Público" del Ministerio de Sanidad, Servicios Sociales e Igualdad). En este periodo, el gasto sanitario público ha decrecido en promedio a razón de un 1,05% anual, mientras que el gasto por habitante también ha decrecido a razón de un 1,64%. Las consecuencias sobre las oficinas de farmacia, entre otras, han sido: el empeoramiento de la situación financiera, la disminución de los márgenes comerciales y el importe del ticket medio. Para añadir algo más de intranquilidad al mercado farmacéutico, el propio modelo de farmacia español no ha estado exento de turbulencias en los últimos años. La primera década del siglo XXI ha supuesto para el sector farmacéutico de los países mediterráneos una época de intranquilidad pues su modelo de establecimiento farmacéutico ha sido cuestionado severamente por las autoridades de la Unión Europea. Por todo ello, sin dejar de lado su rol como actor promotor de la salud dentro del sistema sanitario, entendemos que su negocio debe ser próspero para que la prestación de servicios (dispensación, atención farmacéutica, formación, información, asesoramiento, etc.) se desarrollen de la forma más eficaz y eficiente posible. Analizada la situación actual, hemos observado que es inexistente la utilización de técnicas y algoritmos de segmentación de usuarios e incipiente la distinción entre clientes y pacientes en las oficinas de farmacia. Es por este motivo, que proponemos esta investigación con el objetivo principal de encontrar un modelo de caracterización de los usuarios (clientes y pacientes) de una oficina de farmacia en base a variables discriminantes y con la ayuda de un algoritmo de segmentación bioinspirado adecuado y contrastado. Identificados los segmentos homogéneos de sus clientes, se pueden desarrollar estrategias personalizadas para atenderlos. Para la caracterización de usuarios, se propone el uso de técnicas estadísticas de clustering basadas en algoritmos metaheurísticos, previa comparación con otros algoritmos, esperando que el resultado sea satisfactorio en la caracterización de los usuarios en bases de datos de grandes dimensiones. Desde el punto de vista de metodológico, se describe el algoritmo de segmentación propuesto y se realizan diferentes ejecuciones de él, con un software desarrollado ex profeso, en diferentes conjuntos de datos para probar sus prestaciones. Además se detalla la encuesta realizada a farmacéuticos de la provincia de Sevilla, como método e instrumento para apoyar la conveniencia de esta investigación. Como principales conclusiones, se obtiene que DECCS, el algoritmo bionspirado propuesto, mejora las prestaciones de los algoritmos clásicos seleccionados, tanto en los problemas utilizados de tamaño medio como en los de gran tamaño. Con respecto a su predecesor ACDE, lo supera en tres de los cuatro problemas de gran tamaño seleccionados. Con lo que se muestra un algoritmo solvente y eficaz para problemas de mayor tamaño. Esta conclusión se ve reforzada por el hecho de que en el caso de los problemas de tamaño medio, ambos tienen comportamientos similares. La aplicación de DECCS a una base de datos del año 2014 completo, correspondiente a una oficina de farmacia, ha resultado un éxito. El algoritmo ha caracterizado dos grupos claros de clientes, según el tipo de producto retirado en la farmacia: medicamento o producto de venta libre (incluyendo medicamentos sin receta "OTC").Universidad Pablo de Olavide. Departamento de Economía, Métodos Cuantitativos e Historia Económic